• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1219-1226
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm because discriminant and prediction system for Red Tide is insufficient development and the study of red tide are focused for the investigation of chemical and biological causing. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for Red Tide. We used K-Nearest Neighbor algorithm for recommend best similar case and input 375 EA by case for Red Tide case base. As a result, conducted 10-fold cross verification for minimal impact from learning data and acquired confidence, we obtained about 84.2% average accuracy for Red Tide case and the best performance results in case by number of similarity classification k is 5. And, we implemented Red Tide monitoring system using inference result.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.461-467
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• 2022

This research is to raise awareness of food safety by designing and supporting a hazard food information notification platform for consumers. To this end, the design was carried out by dividing the process into a data extraction process, an application screen design process, and a CNN-based food inference process. Data was collected through public data APIs and crawling, and it was sent to each activity screen designed for Android studios so that it could be output. As a result, when the platform is executed, information on hazardous food names, registration dates, food classification, manufacturing dates, recovery grades, recovery reasons, recovery methods, company names, barcode numbers, and packaging units can be intuitively and conveniently checked. In addition, CNN-based food inference processes allowed mobile cameras to infer harmful food and applied various quantization techniques such as Dynamic Range, Integer, and Float16 to compare the degree of improvement in inference performance. As a result, the group that applied basic quantization and treated device resources with GPU showed the greatest improvement in inference performance. Through this platform, it is expected that the reliability of food safety will be improved by making it more convenient for consumers to recognize food risks.

• School Mathematics
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• v.14 no.1
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• pp.85-107
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• 2012

Problem solving is important in school mathematics as the means and end of mathematics education. In elementary school, inductive reasoning is closely linked to problem solving. The purpose of this study was to examine ways of improving problem solving ability through analysis of inductive reasoning process. After the process of inductive reasoning in problem solving was analyzed, five different stages of inductive reasoning were selected. It's assumed that the flow of inductive reasoning would begin with stage 0 and then go on to the higher stages step by step, and diverse sorts of additional inductive reasoning flow were selected depending on what students would do in case of finding counter examples to a regulation found by them or to their inference. And then a case study was implemented after four elementary school students who were in their sixth grade were selected in order to check the appropriateness of the stages and flows of inductive reasoning selected in this study, and how to teach inductive reasoning and what to teach to improve problem solving ability in terms of questioning and advising, the creation of student-centered class culture and representation were discussed to map out lesson plans. The conclusion of the study and the implications of the conclusion were as follows: First, a change of teacher roles is required in problem-solving education. Teachers should provide students with a wide variety of problem-solving strategies, serve as facilitators of their thinking and give many chances for them ide splore the given problems on their own. And they should be careful entegieto take considerations on the level of each student's understanding, the changes of their thinking during problem-solving process and their response. Second, elementary schools also should provide more intensive education on justification, and one of the best teaching methods will be by taking generic examples. Third, a student-centered classroom should be created to further the class participation of students and encourage them to explore without any restrictions. Fourth, inductive reasoning should be viewed as a crucial means to boost mathematical creativity.

• Journal of KIISE
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• v.43 no.3
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• pp.370-379
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• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.488-501
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. But such services are usually limited to those served on a single mobile device. To resolve the resource limitation problem of mobile devices, a nearby resource sharing research has been studied. Also, not only the nearby resource share but also a resource recommendation through context-based resource reasoning has been studied such as an UMO Project. The UMO Project share and manage the various context information for the personalization resource recommendation and reason based on current context information. Also, should verify resource inference rules for reliable the resource recommendation. But, to create various context information requires huge cost and time in actuality. Thus, we propose a inference verification tool called USim to resolve problem. The proposed inference verification tool provides convenient graphic user interfaces and it easily creates context information. The USim exactly verifies new inference rules through dynamic changes of context information.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Journal of KIISE
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• v.42 no.8
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• pp.998-1009
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• 2015

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.